• DOI:10.3847/1538-4365/ab9a3a
Corpus ID: 219980601

Identification of single spectral lines through supervised machine learning in a large HST survey (WISP): a pilot study for Euclid and WFIRST

@article{Baronchelli2020IdentificationOS,
  title={Identification of single spectral lines through supervised machine learning in a large HST survey (WISP): a pilot study for Euclid and WFIRST},
  author={I. Baronchelli and C. M. Scarlata and G. Rodighiero and L. Rodr{\'i}guez-Mu{\~n}oz and M. Bonato and M. Bagley and A. Henry and M. Rafelski and M. Malkan and J. Colbert and Y. Dai and H. Dickinson and C. Mancini and V. Mehta and L. Morselli and H. I. Teplitz},
  journal={arXiv: Astrophysics of Galaxies},
  year={2020}
}

I. Baronchelli, C. M. Scarlata, +13 authors H. I. Teplitz

Published 2020

Physics

arXiv: Astrophysics of Galaxies

Future surveys focusing on understanding the nature of dark energy (e.g., Euclid and WFIRST) will cover large fractions of the extragalactic sky in near-IR slitless spectroscopy. These surveys will detect a large number of galaxies that will have only one emission line in the covered spectral range. In order to maximize the scientific return of these missions, it is imperative that single emission lines are correctly identified. Using a supervised machine-learning approach, we classified a… CONTINUE READING

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